• Corpus ID: 237503417

Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data

@article{Das2021ConditionalSD,
  title={Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data},
  author={Hari Prasanna Das and Ryan Tran and Japjot Singh and Xiangyu Yue and Geoffrey H. Tison and Alberto L. Sangiovanni-Vincentelli and Costas J. Spanos},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.06486}
}
$\textbf{Background:}$ At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine Learning (ML) models trained with the available data, which is limited in quantity and poor in diversity, will often be biased and inaccurate. At the same time, ML algorithms designed to fight pandemics must have good performance and be developed in a time-sensitive manner. To tackle the challenges of limited data, and… 

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